A Framework To Detect Brain Tumor Cells Using MRI Images

Tumor indicates unfettered presence of a cluster of cells in a specific area of the body part. Brain tumor is considered one of the most common tumors for both men and women and can lead to high death risk if patients fail to obtain appropriate medical treatment. In order to diagnose brain tumors, electronic modalities are integrated and among them MRI is a popular one. For MRI brain tumor region analysis segmentation, detection and classification are considered as important steps in digital imaging pathology laboratory. Existing state-of-the-art approaches demand widespread amount of supervised training data from pathologists and may still accomplish poor results in images from unseen tissue types. A suitable framework has been presented in this study to identify brain tumor cells for MRI images. In this study for the first time in compare to all other existing accessible approaches morphological operations has been incorporated to eliminate undesirable regions and to assist segmentation and identification of region of interests. Compared with existing state-of the-art supervised models, our method generalizes considerably improved identified results on brain tumor cells deprived of training data. Even with training data, our approach attains the identical performance without supervision cost. This study results indicates an accuracy rate above 96.23% accuracy associated to existing works.